Sirshendu Ganguly
056ade0661
- Use librosa instead of torchaudio for mediaStorage - Created dedicated toggle handlers for Image Inference, Video Inference, Upscaler, Audio Inference, Accelerator Options, Bytedance Provider Settings, and OpenAI Provider Settings - Fixed Audio Inference widget ordering (use parameters now appear before their corresponding parameters) - All handlers use the same proven pattern from videoInferenceDimensionsHandler - Improved widget disabling/enabling functionality across all nodes
70 lines
2.7 KiB
Python
70 lines
2.7 KiB
Python
from .utils import runwareUtils as rwUtils
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from typing import List
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class videoInferenceInputs:
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"""Video Inference Inputs node for configuring video generation inputs"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {},
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"optional": {
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"Image": ("IMAGE", {
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"tooltip": "Portrait image for video generation. This will be the main subject that will speak and move in the generated video."
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}),
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"Audio": ("STRING", {
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"tooltip": "Connect the mediaUUID output from Runware Media Upload node with audio file. This audio will be synchronized with the generated video."
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}),
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"Video": ("STRING", {
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"tooltip": "Connect the mediaUUID output from Runware Media Upload node with a reference/input video."
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}),
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"Mask": ("IMAGE", {
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"tooltip": "Mask image to specify a specific subject in the image to speak. Use white/black mask format."
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}),
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"Frame": ("IMAGE", {
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"tooltip": "Frame image for video generation. Connect a Load Image node to provide the frame image."
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}),
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"References": ("RUNWAREVIDEOINPUTSREFERENCES", {
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"tooltip": "Connect the Video Inputs References node to provide reference images."
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}),
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}
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}
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DESCRIPTION = "Configure custom inputs for Runware Video Inference with OmniHuman 1.5 support, including image, audio, video, and mask inputs."
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FUNCTION = "createInputs"
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RETURN_TYPES = ("RUNWAREVIDEOINFERENCEINPUTS",)
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RETURN_NAMES = ("Video Inference Inputs",)
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CATEGORY = "Runware"
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def createInputs(self, **kwargs):
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"""Create video inference inputs from provided parameters"""
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image = kwargs.get("Image", None)
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audio = kwargs.get("Audio", None)
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video = kwargs.get("Video", None)
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mask = kwargs.get("Mask", None)
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frame = kwargs.get("Frame", None)
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references = kwargs.get("References", None)
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inputs = {}
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if image is not None:
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inputs["image"] = rwUtils.convertTensor2IMG(image)
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if audio is not None and audio.strip() != "":
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inputs["audio"] = audio.strip()
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if video is not None and video.strip() != "":
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inputs["video"] = video.strip()
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if mask is not None:
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inputs["mask"] = rwUtils.convertTensor2IMG(mask)
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if frame is not None:
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inputs["frame"] = rwUtils.convertTensor2IMG(frame)
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if references is not None and len(references) > 0:
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inputs["references"] = references
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return (inputs,)
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